Tag Archives: kate crawford

February 26, 2014 | 7 Comments

Tell Me More danah boyd: an interview with the author of “It’s Complicated: The Social Lives of Networked Teens”


MSR3sm-sq danah boyd (@zephoria) is a Principal Researcher at Microsoft Research, a Research Assistant Professor in Media, Culture, and Communication at New York University, and a Fellow at Harvard’s Berkman Center. In 2009 Fast Company named boyd one of the most influential women in technology. Also in 2010, Fortune named her the smartest academic in the technology field and “the reigning expert on how young people use the Internet.” Foreign Policy named boyd one of its 2012 Top 100 Global Thinkers “for showing us that Big Data isn’t necessarily better data”. danah just published, It’s Complicated: The Social Lives of Networked Teens.  

There’s this idea that hard-core techies are code geeks. But hard-core techies also look like ethnographers. A tech ethnographer not only has to understand cultural code, but the mechanisms for how software design links back up to tech practices. I sat down with one of the most well known tech ethnographers of our time, danah boyd (@zephoria). 

Over breakfast at The Ace Hotel’s Breslin, danah and I talked about her career. This fascinating and personal interview reveals danah’s journey through industry and academia.

We’re also excited to have danah’s interview launch Ethnography Matter’s second column, Tell Me More,  featuring interviews with people who are pushing the boundaries of ethnography in unconventional and exciting ways. We conduct the first interview and then post a follow up interview with crowd-sourced questions from the audience. 

Post your follow-up question for danah in the comments or tweet it with the hashtag #askdanah by March 10. danah will select her favorite questions to answer in her second interview!  

Tricia: danah, I’m super excited that we get to talk ethnography over some yummy breakfast food! Earlier last year, you were inducted into the SXSW Hall of Fame.  An ethnographer being validated by geeks! I was beyond excited when I heard this news. How did you feel when you found out?

danah: SXSW has been a very important event to me for a long time. I learned so much about the tech industry through that conference by spending late nights drinking with entrepreneurs and makers. I actually got many a job that way too. It was at SXSW where Ev Williams and I started debating blogging practices. He hired me to work for him that summer.  Oh, and SXSW was where I met my partner.

Tricia: What? Are you serious?

danah: ::laugh:: Ayup!  And now we have a baby who we’re taking back to SXSW this year.

Tricia: Shut up. That is so sweet. Where did you guys meet at SXSW?

danah. At a Sleater-Kinney show.

Tricia: That’s awesome.

danah: It’s just funny to be honored there because I’ve selfishly gotten so much out of the conference.

Tricia: Well I remember very clearly when I read the transcript of the keynote you delivered at SXSW in 2010. It was about Facebook’s issues with privacy. Your talk generated so much discussion. How did you settle on this topic?

danah: I thought, what could I do that would provoke this audience to think? I saw it as a political platform; not big P but small p. I wanted to use this opportunity to challenge norms inside tech industry. I decided to take on the underlying values and beliefs in tech industry regarding privacy because my research was showing that the rhetoric being espoused was naïve. My topic was not surprising for academics, but it was for practitioners.Read More… Tell Me More danah boyd: an interview with the author of “It’s Complicated: The Social Lives of Networked Teens”

May 13, 2013 | 43 Comments

Big Data Needs Thick Data


Tricia Wang

Tricia Wang

Editor’s Note: Tricia provides an excellent segue between last month’s “Ethnomining” Special Edition and this month’s on “Talking to Companies about Ethnography.” She offers further thoughts building on our collective discussion (perhaps bordering on obsession?) with the big data trend. With nuance she tackles and reinvents some of the terminology circulating in the various industries that wish to make use of social research. In the wake of big data, ethnographers, she suggests, can offer thick data. In the face of derisive mention of “anecdotes” we ought to stand up to defend the value of stories.

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image from Mark Smiciklas at Intersection Consulting

image from Mark Smiciklas at Intersection Consulting

Big Data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG?

The positivistic bias in favor of Big Data (a term often used to describe the quantitative data that is produced through analysis of enormous datasets) as an objective way to understand our world presents challenges for ethnographers. What are ethnographers to do when our research is seen as insignificant or invaluable? Can we simply ignore Big Data as too muddled in hype to be useful?

No. Ethnographers must engage with Big Data. Otherwise our work can be all too easily shoved into another department, minimized as a small line item on a budget, and relegated to the small data corner. But how can our kind of research be seen as an equally important to algorithmically processed data? What is the ethnographer’s 10 second elevator pitch to a room of data scientists?

…and GO!

Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data.

Lacking the conceptual words to quickly position the value of ethnographic work in the context of Big Data, I have begun, over the last year, to employ the term Thick Data (with a nod to Clifford Geertz!) to advocate for integrative approaches to research. Thick Data uncovers the meaning behind Big Data visualization and analysis.

Thick Data: ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.

Thick Data analysis primarily relies on human brain power to process a small “N” while big data analysis requires computational power (of course with humans writing the algorithms) to process a large “N”. Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.

Read More… Big Data Needs Thick Data